“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

All one needs to know about fog computing and related edge computing paradigms: A complete survey

https://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=1007&context=aii_fac_pub

Machine Learning for Internet of Things Data Analysis: A Survey

The Internet of Things (IoT) provides the ability for humans and computers to learn and interact from billions of things that include sensors, actuators, services, and other Internet-connected objects. The realization of IoT systems will enable seamless integration of the cyber world with our physical world and will fundamentally change and empower human interaction with the world. A key technology in the realization of IoT systems is middleware, which is usually described as a software system designed to be the intermediary between IoT devices and applications. In this paper, we first motivate the need for an IoT middleware via an IoT application designed for real-time prediction of blood alcohol content using smartwatch sensor data. This is then followed by a survey on the capabilities of the existing IoT middleware. We further conduct a thorough analysis of the challenges and the enabling technologies in developing an IoT middleware that embraces the heterogeneity of IoT devices and also supports the essential ingredients of composition, adaptability, and security aspects of an IoT system.

IoT Middleware: A Survey on Issues and Enabling Technologies

Social Internet of Things (SIoT) is a new paradigm where Internet of Things (IoT) merges with social networks, allowing people and devices to interact, and facilitating information sharing. However, security and privacy issues are a great challenge for IoT but they are also enabling factors to create a “trust ecosystem.” In fact, the intrinsic vulnerabilities of IoT devices, with limited resources and heterogeneous technologies, together with the lack of specifically designed IoT standards, represent a fertile ground for the expansion of specific cyber threats. In this paper, we try to bring order on the IoT security panorama providing a taxonomic analysis from the perspective of the three main key layers of the IoT system model: 1) perception; 2) transportation; and 3) application levels. As a result of the analysis, we will highlight the most critical issues with the aim of guiding future research directions.

Evaluating Critical Security Issues of the IoT World: Present and Future Challenges

Fault detection for closed-loop control systems is the future development in the field of the fault diagnosis. Since a closed-loop control system is generally very robust to the external disturbances, fault detection has been challenging a hot research area. Traditional data-driven detection methods are not particularly designed for closed-loop control systems and thus can be improved. In this paper, a new fault detection method is proposed, which is based on the parity space for the closed-loop control system. The main principle of our method is to transform the detection residual into the parity space of the original space to restrict false detection or leak detection caused by the estimation of uncertain states. More specifically, the construction of the stable kernel matrix in the parity space is given, and the residual sequence is accumulated to improve the fault-to-noise ratio and thus increase the detection performance. To verify our method, we have conducted a simulation which is based on a numerical simulation model and the Tennessee industrial system respectively. The results show that the proposed method is more feasible and more effective in fault detection for closed-loop control systems compared with the traditional data-driven detection methods, including the time series modeling method and the partial least squares method.

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Fault Detection for Closed-Loop Control Systems Based on Parity Space Transformation

The significant growth and adoption of Internet of Things (IoT) solutions has led to tremendous increase in the generation of data. The need for high speed data processing has become very important to meet with the ever increasing volume and velocity of IoT data, due to the large scale and distributed nature of IoT infrastructure and networks. Present cloud based technologies are struggling to meet up with these needs for real time data processing in the midst of enormous amounts of data. The success of bitcoin has inspired more research in the application of Distributed ledger technologies in various domains. The decentralized nature of these platforms have enabled security and privacy of data in previous research and their architecture has a potential for enabling large scale decentralized data processing. In this paper, we identify some open areas of research in the use of distributed ledger technology and propose a framework for storing, analyzing and ensuring the security of large volumes of IoT data.

Research Directions on Big IoT Data Processing Using Distributed Ledger Technology: A Position Paper

XML data broadcast is attracting more and more interests from many researchers. In on-demand XML data broadcast, the existing broadcast scheduling methods usually only considered the document-selectivity of user requests. In this paper, we propose an efficient document-split algorithm which considers the branch-selectivity for on-demand XML data broadcast scheduling. First, we divide the user requests into two sets according to the branch-selectivity; then split every document into two parts according to these two sets. With our scheduling algorithm, the hot and small branches are broadcast first. User requests with low branch-selectivity can be satisfied in a shorter time and both of access time and tuning time are reduced. Experiments show that our algorithm provides significantly better performance when compared with existing scheduling algorithms.

An efficient document-split algorithm for on-demand XML data broadcast scheduling

The data placement strategy greatly affects the efficiency of MapReduce. The current strategy only takes the map phase into account to optimize the map time. But the ignored shuffle phase may increase the total running time significantly in many jobs. We propose a new data placement strategy, named OPTAS, which optimizes both the map and shuffle phases to reduce their total time. However, the huge search space makes it difficult to find out an optimal data placement instance (DPI) rapidly. To address this problem, an algorithm is proposed which can prune most of the search space and find out an optimal result quickly. The search space firstly is segmented in ascending order according to the potential map time. Within each segment, we propose an efficient method to construct a local optimal DPI with the minimal total time of both the map and shuffle phases. To find the global optimal DPI, we scan the local optimal DPIs in order. We have proven that the global optimal DPI can be found as the first local optimal DPI whose total time stops decreasing, thus further pruning the search space. In practice, we find that at most fourteen local optimal DPIs are scanned in tens of thousands of segments with the pruning strategy. Extensive experiments with real trace data verify not only the theoretic analysis of our pruning strategy and construction method but also the optimality of OPTAS. The best improvements obtained in our experiments can be over 40% compared with the existing strategy used by MapReduce.

OPTAS: Optimal Data Placement in MapReduce

Interacting multiple model (IMM) filter is a classical method to track targets in hybrid situations. However, it can exhibit divergence when the models are correlated or the system suffers from uncertainties. The generalized covariance intersection method based on the weighted Kullback–Leibler (K–L) divergence can solve the divergence problem of correlated estimates. A novel interacting multiple model (NIMM) filter is presented that combines two different algorithms, the adaptive fading Kalman filter and the maximum correntropy Kalman filter, based on the model interacting with the weighted K-L divergence to address the uncertainty problems of the system. The NIMM filter algorithm is designed and the stability and accuracy are analyzed. The simulation results demonstrate that the proposed filter can effectively improve the accuracy under different uncertainty conditions for classical examples and ballistic trajectory tracking scenarios.

Yongrui Qin

Papers

Fault Detection for Closed-Loop Control Systems Based on Parity Space Transformation

Research Directions on Big IoT Data Processing Using Distributed Ledger Technology: A Position Paper

An efficient document-split algorithm for on-demand XML data broadcast scheduling

OPTAS: Optimal Data Placement in MapReduce

Novel interacting multiple model filter for uncertain target tracking systems based on weighted Kullback–Leibler divergence